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Integrate Manual Chat Trigger in your LLM apps and 422+ apps and services
Use Manual Chat Trigger to easily build AI-powered applications and integrate them with 422+ apps and services. n8n lets you seamlessly import data from files, websites, or databases into your LLM-powered application and create automated scenarios.
Popular ways to use Manual Chat Trigger integration
AI: Conversational agent with custom tool written in JavaScript
This workflow implements a custom tool via JavaScript code which returns a random color to users and excludes the given colors.
Note that to use this template, you need to be on n8n version 1.19.4 or later.
n8n Team
+2
AI chatbot that can search the web
This workflow is designed for dynamic and intelligent conversational capabilities. It incorporates OpenAI's GPT-4o model for natural language understanding and generation. Additional tools include SerpAPI and Wikipedia for enriched, data-driven responses. The workflow is triggered manually, and utilizes a 'Window Buffer Memory' to maintain the context of the last 20 interactions for better conversational continuity. All these components are orchestrated through n8n nodes, ensuring seamless interconnectivity.
To use this template, you need to be on n8n version 1.50.0 or later.
n8n Team
Use an open-source LLM (via HuggingFace)
This workflow demonstrates how to connect an open-source model to a Basic LLM node.
The workflow is triggered when a new manual chat message appears. The message is then run through a Language Model Chain that is set up to process text with a specific prompt to guide the model's responses.
Note that open-source LLMs with a small number of parameters require slightly different prompting with more guidance to the model.
You can change the default Mistral-7B-Instruct-v0.1 model to any other LLM supported by HuggingFace. You can also connect other nodes, such as Ollama.
Note that to use this template, you need to be on n8n version 1.19.4 or later.
n8n Team
+5
Ask questions about a PDF using AI
The workflow first populates a Pinecone index with vectors from a Bitcoin whitepaper. Then, it waits for a manual chat message. When received, the chat message is turned into a vector and compared to the vectors in Pinecone. The most similar vectors are retrieved and passed to OpenAI for generating a chat response.
Note that to use this template, you need to be on n8n version 1.19.4 or later.
David Roberts
+4
OpenAI assistant with custom tools
This workflow shows how you can get your OpenAI assistant to call an n8n workflow as a tool. Since you can put almost any functionality in an n8n workflow, this means you can give your assistant access to almost any data source.
Note that to use this template, you need to be on n8n version 1.19.4 or later.
David Roberts
+8
AI Crew to Automate Fundamental Stock Analysis - Q&A Workflow
How it works:
Using a Crew of AI agents (Senior Researcher, Visionary, and Senior Editor), this crew will automatically determine the right questions to ask to produce a detailed fundamental stock analysis.
This application has two components: a front-end and a Stock Q&A engine.
The front end is the team of agents automatically figuring out the questions to ask, and the back-end part is the ability to answer those questions with the SEC 10K data.
This template implements the Stock Q&A engine.
For the front-end of the application, you can choose one of two options:
using CrewAI with the Replit environment (code approach)
fully visual approach with n8n template (AI-powered automated stock analysis)
Setup steps:
Use first workflow in template to upsert a company annual report PDF (such as from SEC 10K filling)
Get URL for Webhook in second workflow template
CrewAI front-end: Youtube overview video
Fork this AI Agent environment Crew Agent Environment
Set the webhook URL into N8N_WEBHOOK_URL variable
Set OpenAI_API_KEY variable
Derek Cheung
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Manual Chat Trigger Node Docs + Examples
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